First came single-factor exchange-traded funds. These funds targeted individual factors associated with attractive long-term risk-adjusted returns. While many have become comfortable with the underpinnings of factors like value and momentum and how investors try to harness them in practice, it can be difficult to understand how to combine individual factor funds. More recently, fund providers have launched a bevy of multifactor funds that offer investors prepackaged factor combinations. Why might an investor opt for a fund that fuses factors together on their behalf? Can this approach add value relative to a do-it-yourself approach of mixing single-factor funds? And what is the best approach to combining factors in a multifactor framework? I’ll set out to answer these questions here.
The case for diversification extends to factor investing. The article “The Case for Multifactor ETFs,” details the benefits of spreading one’s factor bets. Individual factors can underperform their factor peers and the broader market for prolonged periods of time. Combining factors with low correlations to one another will yield a more stable risk/return proposition relative to owning any one factor-focused fund in isolation. This may also yield all-important behavioral benefits. A smoother ride may help investors to stay the course when a particular factor experiences a dry spell. But factor diversification is easier said than done. When it comes to selecting multifactor funds, there are several decisions to consider:
- Which factors are in the mix?
- How are the fund’s factor exposures measured and constructed?
- How are weightings assigned to each factor?
- How are these factors combined?
Generally speaking, the largest multifactor ETFs set out to exploit well-documented factors and measure them using sensible inputs. Most of these funds’ underlying indexes then either equal-weight their factor exposures or assign them static weightings. There are also a handful of multifactor ETFs in the works that will attempt to time their factor exposures. Once these factor building blocks are chosen, defined, and sized, the next step is to determine how to best combine them.
Shaken, Not Stirred
When it comes to combining factors, there are two primary approaches: isolated (which may also be referred to as portfolio mixing or blending) and integrated (also referred to as factor signal blending). The isolated approach builds separate single-factor portfolios and combines them into one portfolio. For example, Goldman Sachs ActiveBeta U.S. Large Cap Equity ETF (GSLC, listed in the U.S.) uses an isolated approach to combining factors. Proponents of the isolated approach tout its transparency and lower tracking error compared with the broader market. Investors won’t stray too far from a market-cap-weighted benchmark and can better assess each individual factors’ performance contribution. The drawbacks of this approach are that it results in relatively muted factor exposures, which somewhat diminishes their potential to outperform the broad market.
The integrated approach ranks stocks based on a composite score that combines all factor signals simultaneously. For example, iShares Edge MSCI Multifactor USA (LRGF, listed in the U.S.) integrates factor signals to construct its multifactor fund. Building the portfolio in one fell swoop results in more-pronounced tilts toward the targeted factors. This leads to higher tracking error versus the broader market and increases the prospect of greater long-term out- or underperformance. The drawbacks of the integrated approach are that its bigger factor bets may lead to extended stretches of underperformance. Also, this process tends to be more opaque relative to the isolated approach.
Because these funds have short live track records, it can be difficult to determine which method (isolated or integrated) results in an optimal combination of factor exposures. Also, most multifactor funds differ across a number of dimensions—not just in their approach to factor combination. For example, GSLC and LRGF don’t target the exact same factors and don’t measure their shared factors identically. LRGF targets the small-size premium while GSLC does not, and GSLC includes the low-volatility factor while LRGF does not. These differences make it difficult to assess the true impact of taking either an isolated or integrated approach to combining factors.
In part 2 of this article, we will try to assess how the isolated approach and integrated approache have flared.
